Adaptive type-2 fuzzy control of non-linear systems

Abstract

The paper describes the development of two different type-2 adaptive fuzzy logic controllers and their use for the control of a non linear system that is characterized by the presence of bifurcations and parameter uncertainty. Although a type-2 fuzzy logic controller is able to handle the non linearities and the uncertainties present in a system, its robustness and effectiveness can be increased by the use of an opportune adaptive algorithm. A simulation study was conducted to compare the behavior of adaptive controllers with that of simple type-1 and type-2 fuzzy logic controllers. The system to be controlled, used for the simulation, is a continuous bioreactor for the treatment of mixed wastes in which a culture of Pseudomonas Putida is carried out while phenol and glucose are carbon and energy sources. From simulations results it can be seen that both adaptive controllers, but in particular the self tuning controller, have a better performance being able to eliminate oscillations that are present with basic fuzzy controllers.

Stabilization of Inter-Area Oscillations in a Two-Area Test System via Interval Type-2 Fuzzy Based Dynamic Brake Control

“…It is widely-confirmed that conventional linear-based controllers, such as PI and PID controllers, are not able to perform effectively when controlling systems to be with high degrees of nonlinearities and uncertainties [14]. Moreover, changing the system parameters would much more likely cause dire consequences on the stability of the controlled system [14].…”

“…It is widely-confirmed that conventional linear-based controllers, such as PI and PID controllers, are not able to perform effectively when controlling systems to be with high degrees of nonlinearities and uncertainties [14]. Moreover, changing the system parameters would much more likely cause dire consequences on the stability of the controlled system [14]. Subsequently, nonlinear intelligent controllers such as fuzzy logic controllers have been exceedingly employed for controlling nonlinear systems because they are much more robust than linear controllers and are able to handle system parameters changes in an appropriate manner [14].…”

“…Moreover, changing the system parameters would much more likely cause dire consequences on the stability of the controlled system [14]. Subsequently, nonlinear intelligent controllers such as fuzzy logic controllers have been exceedingly employed for controlling nonlinear systems because they are much more robust than linear controllers and are able to handle system parameters changes in an appropriate manner [14]. Fuzzy logic controllers could be categorized to two main categories, Type-1 fuzzy logic controllers and Type-2 fuzzy [15].…”

کنترل لرزه ای پل بزرگراه محک با بهره گیری از سیستم کنترل نیمه فعال و کنترل‌ کننده فازی خودتنظیم

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